In network functions virtualization (NFV), network functions are operated in software as virtual network functions (VNFs) instead of dedicated hardware. The most important issues that need to be addressed in NFV are where the VNFs should be placed in the network, as well as what amount of resources should be assigned to each VNF. Evolvable VNF placement (EvoVNFP) is a meta-algorithm that we previously proposed for controlling an underlying iterative VNF placement method. EvoVNFP realizes better adaptability to regular demand changes by mimicking biological evolution under time-varying environments leading to faster generation of placements. We provide detailed evaluation studies about the mechanism of EvoVNFP and show that iterative placement methods combined with EvoVNFP can generate placements that adapt better to varying goals because of triggers. Numerical results verify that EvoVNFP is able to reduce the required number of calculation steps by up to 48%.